Back

Bovine colostrum-derived extracellular vesicles impair cancer cell proliferation through transcriptional repression.

Huesa Carballo, C. B.; Puga, B.; Bravo, S.; Rana, I.; Hermida, B.; Antelo, M. T.; Lopez Lopez, R.; Abal, M.; Barbazan, J.

2025-10-07 cancer biology
10.1101/2025.10.07.680845 bioRxiv
Show abstract

Milk-derived extracellular vesicles (EVs) are a promising source of molecules with therapeutic potential. Bovine colostrum is particularly enriched in EVs, which carry a unique cargo of proteins involved in immune regulation, development, and cellular signaling. However, despite promising bioactive effects in various fields, little is known about their potential as anti-cancer agents. Here, we demonstrate that colostrum-derived EVs (Col-EVs) exert a potent and specific anti-proliferative effect on gastrointestinal cancer cell lines, independent of apoptosis induction. Using a multi-modal approach combining proteomics and functional assays, we show that Col-EVs induce a reversible proliferative arrest through transcriptional repression and chromatin and nuclear remodeling. Col-EV treatment leads to widespread dysregulation of RNA processing and transcriptional machinery, including the downregulation of splicing factors and chromatin regulators essential for cell cycle progression. These molecular changes are accompanied by chromatin compaction, nuclear reorganization, and cytoskeletal remodeling. Notably, these effects do not induce cell death and are reversible upon EV removal, suggesting a modulatory mechanism rather than cytotoxicity. Furthermore, Col-EVs enhance the efficacy of DNA-targeting chemotherapies such as 5-fluorouracil, indicating their potential as adjunctive agents in cancer treatment. Overall, our findings reveal that Col-EVs can selectively and reversibly suppress cancer cell proliferation by reprogramming transcriptional and nuclear architecture, offering a natural, biocompatible strategy for modulating tumor growth and sensitizing cancer cells to conventional therapies.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.